Securing Intelligence: The Strategic Necessity of Air-Gapped AI Systems in the Age of Cloud-Based LLMs
Name
MIT-LIN-151372.pdf
Description
Main Report
Size
242.11 KB
Format
Adobe PDF
Checksum (MD5)
fe9559037bff33dedc8c20959296ebb2
Author(s)
Viggh, Herbert
Tsagaratos, Jennifer
Date Issued
February 17, 2026
Abstract
The increasing use of large language models (LLMs)
in applications, from military strategy to customer service, raises
concerns about data sovereignty, security, and privacy. Cloudbased
API models, created by companies such as OpenAI, pose
significant risks due to training data exposure and prompt
injection attacks, which can compromise sensitive information
and hidden biases that could influence reporting or executive
decision-making processes. Real-world incidents, such as the
leakage of Samsung’s proprietary source code through ChatGPT,
highlight the dangers of relying on cloud providers with complete
visibility into client queries. Furthermore, data localization laws
and regulations, such as the General Data Protection Regulation
(GDPR), underscore the risks associated with outsourcing
intelligence and decision support systems to foreign entities. Airgapped
AI solutions, which run on isolated networks disconnected
from the outside world, offer a secure alternative for sensitive
environments such as national defense, research laboratories,
and critical infrastructure. By maintaining control over AI
processes, organizations can ensure information safety, comply
with regulations, and mitigate risks associated with cloud-based
AI infrastructure, ultimately safeguarding their data integrity,
privacy, and operational independence.
Subjects
large language models (LLMs)
General Data Protection Regulation (GDPR)
AI infrastructure
MIT Department
Lincoln Laboratory
Persistent DSpace Link
